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SBIR/STTR

Satellite Swarm Localization and Control via Random Finite Set Statistics, Phase I

Project Introduction

The proposed novel program will develop and demonstrate a new approach to perform real-time relative vehicle localization within a swarm formation with application to communication-less coordination. These objectives are achieved by using Random Finite Sets statistics theory to solve the multiple object tracking problem. The swarm formation localization problem can be formulated as estimating the local intensity function of targets in the near field and developing probabilistic control strategies to track an expected localization state space configuration. Work will focus on developing estimation and control algorithms that can utilize simple measurements range and bearing angle to other units, and also determine the local environment using feature measurements. Three major tasks are proposed for the development of swarming space vehicle estimation and control: Random Finite Set Localization, Random Finite Set Formation Control, and Bayesian Collective Decision Making. Algorithm development in Phase I will extend to a Hypothesis Density Filter and Sequential Monte Carlo Hypothesis Density Filter, Motion Model, Estimation techniques, Landmark SLAM using these techniques, Behavioral Distribution Control, Cyclic Distribution Control, and multiple decision making estimation models. Proposed follow-on efforts will fully implement the swarm technology for real-time integrated system use, identify different formation configurations and sensor combination for hardware integration, and work to position the system for integration into a demonstration mission identified in the Phase I work to fully illustrate the mission enhancements of the operational system.
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Anticipated Benefits

Potential NASA Commercial Applications: NASA applications consist of enabling autonomous precision swarm coordination for satellites traveling in Earth orbit or eventually into deep space, including greater precision for vehicle control. The swarm formation coordination and control algorithms and software will provide expanded mission planning and analysis capabilities, reduction of communication requirements, and reduction of mission risk. The system would offer significant value in providing or augmenting current navigation and control technologies and techniques, as well as reduce support costs and system station-keeping down-time. The system can also offer precise formation control for assisting multiple spacecraft formation flying anywhere in the solar system. The proposed system data product has potential to enable benefits to autonomous planetary rover swarms, asteroid and comet exploration, and earth orbiting swarms.
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